ABSTRACT
Background: SARS-CoV-2 infection causes immune response and produces protective antibodies, and these changes may persist after patients discharged from hospital. Methods: This study conducted a one-year follow-up study on patients with COVID-19 to observe the dynamic changes of circulating leukocyte subsets and virus-specific antibodies. Results: A total of 66 patients with COVID-19 and 213 healthy patients with inactivated SARS-CoV-2 vaccination were included. The virus-specific total antibody, IgG and IgM antibody of patients after one year of recovery were higher than those of healthy vaccinated participants (94.13 vs 4.65, 2.67 vs 0.44, 0.09 vs 0.06, respectively) (P < 0.001). Neutrophil count (OR = 1.73, 95% CI: 1.10-2.70, P = 0.016) and neutrophil-to-lymphocyte ratio (OR = 1.59, 95% CI: 1.05-2.41, P = 0.030) at discharge were the influencing factors for the positivity of virus-specific IgG antibody in patients after one year of recovery. The counts of CD4+ and CD8+ T, B and NK cells increased with the time of recovery, and remained basically stable from 9 to 12 months after discharge. After 12 months, the positivity of IgG antibody was 85.3% and IgM was 11.8%, while the virus-specific antibody changed dynamically in patients within one year after discharge. Conclusions: The SARS-CoV-2 specific antibody of recovered patients showed dynamic fluctuation after discharge, while the leukocyte subsets gradually increased and basically stabilized after 9 months.
Subject(s)
COVID-19 , Antibodies, Viral , COVID-19 Vaccines , Follow-Up Studies , Humans , Immunoglobulin G , Leukocytes , SARS-CoV-2ABSTRACT
BACKGROUND: Coronavirus disease 2019 (COVID-19) is associated with a high mortality rate, especially in patients with severe illness. We conducted a systematic review and meta-analysis to assess the potential predictors of mortality in patients with COVID-19. METHODS: PubMed, EMBASE, the Cochrane Library, and three electronic Chinese databases were searched from December 1, 2019 to April 29, 2020. Eligible studies reporting potential predictors of mortality in patients with COVID-19 were identified. Unadjusted prognostic effect estimates were pooled using the random-effects model if data from at least two studies were available. Adjusted prognostic effect estimates were presented by qualitative analysis. RESULTS: Thirty-six observational studies were identified, of which 27 were included in the meta-analysis. A total of 106 potential risk factors were tested, and the following important predictors were associated with mortality: advanced age, male sex, current smoking status, preexisting comorbidities (especially chronic kidney, respiratory, and cardio-cerebrovascular diseases), symptoms of dyspnea, complications during hospitalization, corticosteroid therapy and a severe condition. Additionally, a series of abnormal laboratory biomarkers of hematologic parameters, hepatorenal function, inflammation, coagulation, and cardiovascular injury were also associated with fatal outcome. CONCLUSION: We identified predictors of mortality in patients with COVID-19. These findings could help healthcare providers take appropriate measures and improve clinical outcomes in such patients.